Why Decentralized Learning
Centralized learning has been the main driver in the machine learning space for a long time. It showed us what machine learning can be. As machine learning algorithms evolved, though, it became clear that centralized learning can’t be the end solution. Begin provides an effective solution in decentralized learning solving some of those issues. Here’s why decentralized learning can fit your business:
Every day, billions of consumer devices (such as watches, phones, tablets, cars, etc.) are being used worldwide. Placing machine learning infrastructure directly on devices reduces the need for server farms. Deep learning models on centralized server farms need loads of energy to function.
Moving the learning to devices that are currently being used dramatically reduces the energy cost of training.
Centralized learning requires transferring user data from their device to the cloud. This poses a massive risk for data leaks and privacy breaches. Decentralized learning keeps the data on the device, reducing the user impact of a breach to a tiny fraction.
Centralized learning only works if the device maintains a low-latency, high throughput connection. This makes it impractical for rural communities, spacefaring vehicles, and other devices that can't hold an internet connection.
Decentralized learning, such as Process on Device, Train Centrally, keeps your devices learning, regardless of reception.
Process on Device, Train Centrally sends a small numerical array representing the dataset being processed, reducing the chances of losing information content which often happens when transferring the entire datasets to a central server and processing the data there.
With Decentralized learning, what used to take months (or years) can now take hours. This groundbreaking technology enables every business to build, improve and scale personalization experiences.
Decentralized learning processes data on the host device and only returns a secure numerical array. This enables you to collaborate across multiple services and businesses without sacrificing proprietary information.